Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images

Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper propose...
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Autor*in:	Fan Zhao [verfasserIn] Haipeng Wang [verfasserIn] Wenda Zhao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Defocus region detection direction-context-inspiration network level-integration

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 64737-64743
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:64737-64743

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2916332

Katalog-ID:	DOAJ051963744

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520			\|a Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance.
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spelling	10.1109/ACCESS.2019.2916332 doi (DE-627)DOAJ051963744 (DE-599)DOAJ1f36b1e88d434a2d8cc044e8eef8d518 DE-627 ger DE-627 rakwb eng TK1-9971 Fan Zhao verfasserin aut Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance. Defocus region detection direction-context-inspiration network level-integration Electrical engineering. Electronics. Nuclear engineering Haipeng Wang verfasserin aut Wenda Zhao verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 64737-64743 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:64737-64743 https://doi.org/10.1109/ACCESS.2019.2916332 kostenfrei https://doaj.org/article/1f36b1e88d434a2d8cc044e8eef8d518 kostenfrei https://ieeexplore.ieee.org/document/8713543/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 64737-64743
allfields_unstemmed	10.1109/ACCESS.2019.2916332 doi (DE-627)DOAJ051963744 (DE-599)DOAJ1f36b1e88d434a2d8cc044e8eef8d518 DE-627 ger DE-627 rakwb eng TK1-9971 Fan Zhao verfasserin aut Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance. Defocus region detection direction-context-inspiration network level-integration Electrical engineering. Electronics. Nuclear engineering Haipeng Wang verfasserin aut Wenda Zhao verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 64737-64743 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:64737-64743 https://doi.org/10.1109/ACCESS.2019.2916332 kostenfrei https://doaj.org/article/1f36b1e88d434a2d8cc044e8eef8d518 kostenfrei https://ieeexplore.ieee.org/document/8713543/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 64737-64743
allfieldsGer	10.1109/ACCESS.2019.2916332 doi (DE-627)DOAJ051963744 (DE-599)DOAJ1f36b1e88d434a2d8cc044e8eef8d518 DE-627 ger DE-627 rakwb eng TK1-9971 Fan Zhao verfasserin aut Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance. Defocus region detection direction-context-inspiration network level-integration Electrical engineering. Electronics. Nuclear engineering Haipeng Wang verfasserin aut Wenda Zhao verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 64737-64743 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:64737-64743 https://doi.org/10.1109/ACCESS.2019.2916332 kostenfrei https://doaj.org/article/1f36b1e88d434a2d8cc044e8eef8d518 kostenfrei https://ieeexplore.ieee.org/document/8713543/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 64737-64743
allfieldsSound	10.1109/ACCESS.2019.2916332 doi (DE-627)DOAJ051963744 (DE-599)DOAJ1f36b1e88d434a2d8cc044e8eef8d518 DE-627 ger DE-627 rakwb eng TK1-9971 Fan Zhao verfasserin aut Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance. Defocus region detection direction-context-inspiration network level-integration Electrical engineering. Electronics. Nuclear engineering Haipeng Wang verfasserin aut Wenda Zhao verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 64737-64743 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:64737-64743 https://doi.org/10.1109/ACCESS.2019.2916332 kostenfrei https://doaj.org/article/1f36b1e88d434a2d8cc044e8eef8d518 kostenfrei https://ieeexplore.ieee.org/document/8713543/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 64737-64743
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topic_facet	Defocus region detection direction-context-inspiration network level-integration Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Fan Zhao @@aut@@ Haipeng Wang @@aut@@ Wenda Zhao @@aut@@
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callnumber-first	T - Technology
author	Fan Zhao
spellingShingle	Fan Zhao misc TK1-9971 misc Defocus region detection misc direction-context-inspiration network misc level-integration misc Electrical engineering. Electronics. Nuclear engineering Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images
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topic_title	TK1-9971 Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images Defocus region detection direction-context-inspiration network level-integration
topic	misc TK1-9971 misc Defocus region detection misc direction-context-inspiration network misc level-integration misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Defocus region detection misc direction-context-inspiration network misc level-integration misc Electrical engineering. Electronics. Nuclear engineering
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title	Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images
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title_sort	deep direction-context-inspiration network for defocus region detection in natural images
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title_auth	Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images
abstract	Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance.
abstractGer	Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance.
abstract_unstemmed	Defocus region detection (DRD) problem aims to assign per-pixel predictions of focus clear areas and defocus blur areas. One of the challenges in this problem is to accurately detect the boundary of the transition region between the focus and defocus regions. To address this issue, the paper proposes a direction-context-inspiration network (DCINet), which can take advantage of the directional context effectively. First, we extract directional context by recurrent neural networks initialized with the identity matrix (IRNN) to weight the feature maps and integrate them in the two-group integration method, which can produce the coarse DRD maps. Second, the maps are level-integrated with the source image guiding and the coarse maps are refined gradually. The overall DCINet can integrate low-level details and high-level semantics efficiently. The Experimental results demonstrate that the network can detect the boundary of the transition region precisely, achieving the state-of-the-art performance.
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title_short	Deep Direction-Context-Inspiration Network for Defocus Region Detection in Natural Images
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